Publications associées à Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Graph-based Methods for Visualization and Clustering

The amount of data that we produce and consume is larger than it has been at any point in the history of mankind, and it keeps growing exponentially. All this information, gathered in overwhelming volumes, often comes with two problematic characteristics: ...

EPFL2017

Compressive Embedding and Visualization using Graphs

Pierre Vandergheynst, Nathanaël Perraudin, Johann Paratte

Visualizing high-dimensional data has been a focus in data analysis communities for decades, which has led to the design of many algorithms, some of which are now considered references (such as t-SNE for example). In our era of overwhelming data volumes, t ...

arXiv e-print archive2017

TimeRank: A dynamic approach to rate scholars using citations

Giovanni Colavizza, Massimo Franceschet

Rating has become a common practice of modern science. No rating system can be considered as final, but instead several approaches can be taken, which magnify different aspects of the fabric of science. We introduce an approach for rating scholars which us ...

Elsevier Science Bv2017

An efficient method for fiber bridging traction identification based on the R-curve: Formulation and experimental validation

John Botsis, Thomas Gmür, Joël Cugnoni, Guillaume Frossard

Fiber bridging is one of the main toughening mechanisms in mode I interlaminar and intralaminar fracture of laminated composites. Intact fibers exert closing forces on both faces of the crack, restraining crack propagation. An effective identification proc ...

Elsevier2017

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic

In this paper, we consider the problem of sequentially optimizing a black-box function

f

based on noisy samples and bandit feedback. We assume that

f

is smooth in the sense of having a bounded norm in some reproducing kernel Hilbert space (RKHS), yield ...

2017

Los desafíos de la modelización hidrológica y la previsión de crecidas en tiempo real en alta montaña

Anton Schleiss, Javier García Hernández, Alain Tommy Foehn

Flood forecasting systems are today recognized as a key element in natural hazard mitigation. The objective is to exploit the available observed and forecasted meteorological information to foresee river discharges up to several days in advance, using a hy ...

2016

Fast Robust PCA on Graphs

Pierre Vandergheynst, Gilles Puy, Nathanaël Perraudin, Nauman Shahid, Vasilis Kalofolias

Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the c ...

Ieee-Inst Electrical Electronics Engineers Inc2016

A randomised primal-dual algorithm for distributed radio-interferometric imaging

Yves Wiaux, Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen

Next generation radio telescopes, like the Square Kilometre Array, will acquire an unprecedented amount of data for radio astronomy. The development of fast, parallelisable or distributed algorithms for handling such large-scale data sets is of prime impor ...

Ieee2016

Correcting for endogeneity using the EMIS method: a case study with revealed preference data

Michel Bierlaire, Anna Fernández Antolín

Endogeneity is an important issue that often arises in discrete choice models leading to biased estimates of the parameters. We propose the extended multiple indicator solution (EMIS) methodology to correct for it and exemplify it with a case study using r ...

2015

Structure Modeling of High Dimensional Data

Mahdad Hosseini Kamal

The digitization of our most common appliances has led to a literal data deluge, some- times referred to as Big Data. The ever increasing volume of data we generate, coupled with our desire to exploit it ever faster, forces us to come up with innovative da ...

EPFL2015

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

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Graph-based Methods for Visualization and Clustering

Compressive Embedding and Visualization using Graphs

TimeRank: A dynamic approach to rate scholars using citations

An efficient method for fiber bridging traction identification based on the R-curve: Formulation and experimental validation

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Los desafíos de la modelización hidrológica y la previsión de crecidas en tiempo real en alta montaña

Fast Robust PCA on Graphs

A randomised primal-dual algorithm for distributed radio-interferometric imaging

Correcting for endogeneity using the EMIS method: a case study with revealed preference data

Structure Modeling of High Dimensional Data

Fast Robust PCA on Graphs

Graph-based Methods for Visualization and Clustering

Correcting for endogeneity using the EMIS method: a case study with revealed preference data

TimeRank: A dynamic approach to rate scholars using citations

Structure Modeling of High Dimensional Data

Compressive Embedding and Visualization using Graphs

Los desafíos de la modelización hidrológica y la previsión de crecidas en tiempo real en alta montaña

A randomised primal-dual algorithm for distributed radio-interferometric imaging

An efficient method for fiber bridging traction identification based on the R-curve: Formulation and experimental validation

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization